Research on Edge Cloud Load Balancing Strategy Based on Chaotic Hierarchical Gene Replication

Edge cloud is used to handle latency-sensitive services. However, due to the large number of concurrent re-quests for edge intensive tasks, the resource allocation strategy affects the stability of nodes. In addition to an adaptive resource allocation model based on chaotic hierarchical gene replication (CRPSO model), the con-cept of chaotic replication ratio is proposed. This study is divided into two parts. The first is to verify the algorithm verification of the simulation platform. By comparison, it is found that CRPSO reduces the CPU and bandwidth utilization by 43.7% and 62.7% on average, respectively, and the memory usage is also lower than other algorithms. Thereafter, we compared the CRPSO algorithm with the Kubernetes clustering algorithm. Experiments showed that the fitness of the CRPSO model is 33.7% higher than that of the com-parison algorithm on average. The algorithm is su-perior to the cluster scheduling algorithm in terms of CPU utilization and memory utilization. Further-more, the total variance of the two resources involved in this model improved significantly, reaching 69.8% on average. In addition, CRPSO also has great advan-tages in other aspects of CPU and memory. It is in-dicated that the model in this study is suitable for the scenario of edge large-scale requests.